Cloud Computing emerged as a major paradigm over the years. Major challenges it poses to computer science are related to latency, scale, and reliability issues. It leverages strong economical aspects and provides sound answers to questions like energy consumption, high availability, elasticity, or efficient computing resource utilization. Many Cloud Computing platform and solution providers resort to virtualization as key underlying technology. Properties like isolation, multi-virtual machine parallelism, load balancing, efficient resource utilization, and dynamic pre-allocation besides economic factors make it attractive. It not only legitimates the spread of several types of data stores supporting a variety of data modes, but also inherently requires different types of load: (i) analytical; (ii) Transactional/Update-intensive; and (iii) mixed real-time feed processing. The authors survey how database systems can best leverage virtualization properties in cloud scenarios. The authors show that read mostly database systems and especially column stores profit from virtualization in analytical and search scenarios. Secondly, cloud analytics virtualized database systems are efficient in transactional scenarios such as Cloud CRM virtualized database systems lag. The authors also explore how the nature of mixed cloud loads can be best reflected by virtualization properties like load balancing, migration, and high availability.
Query Load Balancing in Parallel Database Systems
